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A study on signature verification using a new approach to genetic based machine learning

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4 Author(s)
Yang, X. ; Dept. of Inf. Electron., Nagoya Univ., Japan ; Furuhashi, T. ; Obata, K. ; Uchikawa, Y.

This paper presents a new method to find best features for signature verification. The new method uses a new coding method, a new crossover method, and a new GA method with a local improvement mechanism proposed by the authors. The new coding method is effective to absorb the intra-personal variability among true signatures. The new crossover method determines the number of partial curves chosen for the signature verification. The new GA approach is very efficient in improving the local portions of chromosomes. Experiments are done to show the effectiveness of the new method

Published in:

Systems, Man and Cybernetics, 1995. Intelligent Systems for the 21st Century., IEEE International Conference on  (Volume:5 )

Date of Conference:

22-25 Oct 1995

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